Modeling milk urea of Walloon dairy cows in management perspectives
Bibliographic record
Abstract
The aim of this study was to develop an adapted random regression test-day model for milk urea (MU) and to study the possibility of using predictions and solutions given by the model for management purposes. Data included 607,416 MU test-day records of first-lactation cows from 632 dairy herds in the Walloon Region of Belgium. Several advanced features were used. First, to detect the herd influence, the classical herd x test-day effect was split into 3 new effects: a fixed herd x year effect, a fixed herd x month-period effect, and a random herd test-day effect. A fixed time period regression was added in the model to take into account the yearly oscillations of MU on a population scale. Moreover, first autoregressive processes were introduced and allowed us to consider the link between successive test-day records. The variance component estimation indicated that large variance was associated with the random herd x test-day effect (48% of the total variance), suggesting the strong influence of herd management on the MU level. The heritability estimate was 0.13. By comparing observed and predicted MU levels at both the individual and herd levels, target ranges for MU concentrations were defined to take into account features of each cow and each herd. At the cow level, an MU record was considered as deviant if it was <200 or >400 mg/L (target range used in the field) and if the prediction error was >50 mg/L (indicating a significant deviation from the expected level). Approximately 7.5% of the MU records collected between June 2007 and May 2008 were beyond these thresholds. This combination allowed for the detection of potentially suspicious cows. At the herd level, the expected MU level was considered as the sum of the solutions for specific herd effects. A herd was considered as deviant from its target range when the prediction error was greater than the standard deviation of MU averaged by herd test day. Results showed that 6.7% of the herd test-day MU levels between June 2007 and May 2008 were considered deviant. These deviations seemed to occur more often during the grazing period. Although theoretical considerations developed in this study should be validated in the field, this research showed the potential use of a test-day model for analyzing functional traits to advise dairy farmers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".